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This is the preliminary program for the 2007 Joint Statistical Meetings in Salt Lake City, Utah.

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Activity Number: 475
Type: Contributed
Date/Time: Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #310409
Title: Goodness-of-Fit Tests for Generalized Linear Models When Some Covariates Are Missing
Author(s): Lei Jin*+ and Suojin Wang+
Companies: Texas A&M University and Texas A&M University
Address: Department of Statistics, College Station, TX, 77843, 3143, College Station, TX, 77845,
Keywords: Goodness-of-fit ; Missing at Random ; Generalized Score Statistics ; Nuisance Parameters ; Weighted Estimating Equations
Abstract:

The goodness-of-fit tests for complete samples may encounter difficulties in the presence of missing covariates. We propose goodness-of-fit tests for testing generalized linear models when some covariates are partially missing based on the generalized score statistics. The methods are based on the weighted estimating equations. The effect of nuisance parameters on the generalized score statistic are studied, including the efficiency. The asymptotic distribution of the test statistic is given. Simulation studies are also given in both ordinary linear models and logistic regressions to demonstrate the finite sample performances of this method.

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Revised September, 2007